Feature engineering for electricity load forecasting

Feature engineering for electricity load forecasting#

The purpose of this notebook is to demonstrate how to use skrub and polars to perform feature engineering for electricity load forecasting.

We will build a set of features from different sources:

  • Historical weather data for 10 medium to large urban areas in France;

  • Holidays and calendar features for France;

  • Historical electricity load data for the whole of France.

All these data sources cover a time range from March 23, 2021 to May 31, 2025.

Since our maximum forecasting horizon is 24 hours, we consider that the future weather data is known at a chosen prediction time. Similarly, the holidays and calendar features are known at prediction time for any point in the future.

Therefore, features derived from the weather and calendar data can be used to engineer “future covariates”. Since the load data is our prediction target, we will can also use it to engineer “past covariates” such as lagged features and rolling aggregations.

Environment setup#

We need to install some extra dependencies for this notebook if needed (when running jupyterlite). We need the development version of skrub to be able to use the skrub expressions.

%pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl
%pip install -q altair holidays https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl

ERROR: polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl is not a supported wheel on this platform.


Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

# The following 3 imports are only needed to workaround some limitations
# when using polars in a pyodide/jupyterlite notebook.
import tzdata  # noqa: F401
import pandas as pd
from pyarrow.parquet import read_table

import polars as pl
import skrub
from pathlib import Path
import holidays
import warnings

# Ignore warnings from pkg_resources triggered by Python 3.13's multiprocessing.
warnings.filterwarnings("ignore", category=UserWarning, module="pkg_resources")

Time range#

Let’s define a hourly time range from March 23, 2021 to May 31, 2025 that will be used to join the electricity load data and the weather data. The time range is in UTC timezone to avoid any ambiguity when joining with the weather data that is also in UTC.

We wrap the polars dataframe in a skrub variable to benefit from the built-in TableReport display in the notebook. Using the skrub expression system will also be useful later.

time_range_start = pl.datetime(2021, 3, 23, hour=0, time_zone="UTC")
time_range_end = pl.datetime(2025, 5, 31, hour=23, time_zone="UTC")
time = skrub.var(
    "time",
    pl.DataFrame().with_columns(
        pl.datetime_range(
            start=time_range_start,
            end=time_range_end,
            time_zone="UTC",
            interval="1h",
        ).alias("time"),
    ),
)
time
<Var 'time'>
Show graph Var 'time'

Result:

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To avoid network issues when running this notebook, the necessary data files have already been downloaded and saved in the datasets folder. See the README.md file for instructions to download the data manually if you want to re-run this notebook with more recent data.

data_source_folder = Path("../datasets")
for data_file in sorted(data_source_folder.iterdir()):
    print(data_file)

../datasets/README.md
../datasets/Total Load - Day Ahead _ Actual_202101010000-202201010000.csv
../datasets/Total Load - Day Ahead _ Actual_202201010000-202301010000.csv
../datasets/Total Load - Day Ahead _ Actual_202301010000-202401010000.csv
../datasets/Total Load - Day Ahead _ Actual_202401010000-202501010000.csv
../datasets/Total Load - Day Ahead _ Actual_202501010000-202601010000.csv
../datasets/weather_bayonne.parquet
../datasets/weather_brest.parquet
../datasets/weather_lille.parquet
../datasets/weather_limoges.parquet
../datasets/weather_lyon.parquet
../datasets/weather_marseille.parquet
../datasets/weather_nantes.parquet
../datasets/weather_paris.parquet
../datasets/weather_strasbourg.parquet
../datasets/weather_toulouse.parquet

List of 10 medium to large urban areas to approximately cover most regions in France with a slight focus on most populated regions that are likely to drive electricity demand.

city_names = [
    "paris",
    "lyon",
    "marseille",
    "toulouse",
    "lille",
    "limoges",
    "nantes",
    "strasbourg",
    "brest",
    "bayonne",
]

all_city_weather_raw = {}
for city_name in city_names:
    # all_city_weather_raw[city_name] = skrub.var(
    # f"{city_name}_weather_raw",
    all_city_weather_raw[city_name] = (
        pl.from_arrow(read_table(f"../datasets/weather_{city_name}.parquet"))
    ).with_columns(
        [
            pl.col("time").dt.cast_time_unit(
                "us"
            ),  # Ensure time column has the same type
        ]
    )

all_city_weather_raw["brest"]
shape: (38_688, 7)
timetemperature_2mprecipitationwind_speed_10mcloud_coversoil_moisture_1_to_3cmrelative_humidity_2m
datetime[μs, UTC]f32f32f32f32f32f32
2021-01-01 00:00:00 UTCnullnullnullnullnullnull
2021-01-01 01:00:00 UTCnullnullnullnullnullnull
2021-01-01 02:00:00 UTCnullnullnullnullnullnull
2021-01-01 03:00:00 UTCnullnullnullnullnullnull
2021-01-01 04:00:00 UTCnullnullnullnullnullnull
…………………
2025-05-31 19:00:00 UTC17.51750.012.06945.00.16873.0
2025-05-31 20:00:00 UTC16.26750.09.11447199.00.16877.0
2025-05-31 21:00:00 UTC15.51750.07.55999993.00.16984.0
2025-05-31 22:00:00 UTC15.56750.09.0100.00.1782.0
2025-05-31 23:00:00 UTC15.56750.05.506941100.00.17181.0
all_city_weather_raw["brest"].drop_nulls(subset=["temperature_2m"])
shape: (36_744, 7)
timetemperature_2mprecipitationwind_speed_10mcloud_coversoil_moisture_1_to_3cmrelative_humidity_2m
datetime[μs, UTC]f32f32f32f32f32f32
2021-03-23 00:00:00 UTC4.628null10.086427nullnull94.0
2021-03-23 01:00:00 UTC5.0280.011.1832016.0null95.0
2021-03-23 02:00:00 UTC5.0780.010.9667136.0null94.0
2021-03-23 03:00:00 UTC4.6280.010.4647975.0null93.0
2021-03-23 04:00:00 UTC4.4280.010.4647975.0null92.0
…………………
2025-05-31 19:00:00 UTC17.51750.012.06945.00.16873.0
2025-05-31 20:00:00 UTC16.26750.09.11447199.00.16877.0
2025-05-31 21:00:00 UTC15.51750.07.55999993.00.16984.0
2025-05-31 22:00:00 UTC15.56750.09.0100.00.1782.0
2025-05-31 23:00:00 UTC15.56750.05.506941100.00.17181.0
all_city_weather = time.skb.eval()
for city_name, city_weather_raw in all_city_weather_raw.items():
    all_city_weather = all_city_weather.join(
        city_weather_raw.rename(lambda x: x if x == "time" else x + "_" + city_name),
        on="time",
        how="inner",
    )

all_city_weather = skrub.var(
    "all_city_weather",
    all_city_weather,
)
all_city_weather
<Var 'all_city_weather'>
Show graph Var 'all_city_weather'

Result:

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Calendar and holidays features#

We leverage the holidays package to enrich the time range with some calendar features such as public holidays in France. We also add some features that are useful for time series forecasting such as the day of the week, the day of the year, and the hour of the day.

Note that the holidays package requires us to extract the date for the French timezone.

Similarly for the calendar features: all the time features are extracted from the time in the French timezone.

holidays_fr = holidays.country_holidays("FR", years=range(2019, 2026))

fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris")
calendar = time.with_columns(
    [
        fr_time.dt.date().is_in(holidays_fr.keys()).alias("is_holiday_fr"),
        fr_time.dt.weekday().alias("day_of_week_fr"),
        fr_time.dt.ordinal_day().alias("day_of_year_fr"),
        fr_time.dt.hour().alias("hour_of_day_fr"),
    ],
)
calendar
<CallMethod 'with_columns'>
Show graph Var 'time' CallMethod 'with_columns'

Result:

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Electricity load data#

Finally we load the electricity load data. This data will both be used as a target variable but also to craft some lagged and window-aggregated features.

load_data_files = [
    data_file
    for data_file in sorted(data_source_folder.iterdir())
    if data_file.name.startswith("Total Load - Day Ahead")
    and data_file.name.endswith(".csv")
]

electricity_raw = skrub.var(
    "electricity_raw",
    pl.concat(
        [
            pl.from_pandas(pd.read_csv(data_file, na_values=["N/A", "-"])).drop(
                ["Day-ahead Total Load Forecast [MW] - BZN|FR"]
            )
            for data_file in load_data_files
        ],
        how="vertical",
    ),
)
electricity_raw
<Var 'electricity_raw'>
Show graph Var 'electricity_raw'

Result:

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electricity = (
    electricity_raw.with_columns(
        [
            pl.col("Time (UTC)")
            .str.split(by=" - ")
            .list.first()
            .str.to_datetime("%d.%m.%Y %H:%M", time_zone="UTC")
            .alias("time"),
        ]
    )
    .drop(["Time (UTC)"])
    .rename({"Actual Total Load [MW] - BZN|FR": "load_mw"})
    .filter(pl.col("time").dt.minute().eq(0))
    .filter(pl.col("time") >= time_range_start)
    .filter(pl.col("time") <= time_range_end)
    .select(["time", "load_mw"])
)
electricity
<CallMethod 'select'>
Show graph Var 'electricity_raw' CallMethod 'with_columns' CallMethod 'drop' CallMethod 'rename' CallMethod 'filter' CallMethod 'filter' CallMethod 'filter' CallMethod 'select'

Result:

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electricity.filter(pl.col("load_mw").is_null())
<CallMethod 'filter'>
Show graph Var 'electricity_raw' CallMethod 'with_columns' CallMethod 'drop' CallMethod 'rename' CallMethod 'filter' CallMethod 'filter' CallMethod 'filter' CallMethod 'select' CallMethod 'filter'

Result:

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electricity.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

electricity = electricity.with_columns([pl.col("load_mw").interpolate()])
electricity.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

Check that the number of rows matches our expectations based on the number of hours that separate the first and the last dates. We can do that by joining with the time range dataframe and checking that the number of rows stays the same.

assert (
    time.join(electricity, on="time", how="inner").shape[0] == time.shape[0]
).skb.eval()

Lagged features#

We can now create some lagged features from the electricity load data.

We will create 3 hourly lagged features, 1 daily lagged feature, and 1 weekly lagged feature. We will also create a rolling median and inter-quartile feature over the last 24 hours and over the last 7 days.

def iqr(col, *, window_size: int):
    """Inter-quartile range (IQR) of a column."""
    return col.rolling_quantile(0.75, window_size=window_size) - col.rolling_quantile(
        0.25, window_size=window_size
    )


electricity_lagged = electricity.with_columns(
    [pl.col("load_mw").shift(i).alias(f"load_mw_lag_{i}h") for i in range(1, 4)]
    + [
        pl.col("load_mw").shift(24).alias("load_mw_lag_1d"),
        pl.col("load_mw").shift(24 * 7).alias("load_mw_lag_1w"),
        pl.col("load_mw")
        .rolling_median(window_size=24)
        .alias("load_mw_rolling_median_24h"),
        pl.col("load_mw")
        .rolling_median(window_size=24 * 7)
        .alias("load_mw_rolling_median_7d"),
        iqr(pl.col("load_mw"), window_size=24).alias("load_mw_iqr_24h"),
        iqr(pl.col("load_mw"), window_size=24 * 7).alias("load_mw_iqr_7d"),
    ],
)
electricity_lagged
<CallMethod 'with_columns'>
Show graph Var 'electricity_raw' CallMethod 'with_columns' CallMethod 'drop' CallMethod 'rename' CallMethod 'filter' CallMethod 'filter' CallMethod 'filter' CallMethod 'select' CallMethod 'with_columns' CallMethod 'with_columns'

Result:

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import altair


altair.Chart(electricity_lagged.tail(100).skb.eval()).transform_fold(
    [
        "load_mw",
        "load_mw_lag_1h",
        "load_mw_lag_2h",
        "load_mw_lag_3h",
        "load_mw_lag_1d",
        "load_mw_lag_1w",
        "load_mw_rolling_median_24h",
        "load_mw_rolling_median_7d",
        "load_mw_iqr_24h",
        "load_mw_iqr_7d",
    ],
    as_=["key", "load_mw"],
).mark_line(tooltip=True).encode(x="time:T", y="load_mw:Q", color="key:N").interactive()

Investigating outliers in the lagged features#

Let’s use the skrub.TableReport tool to look at the plots of the marginal distribution of the lagged features.

from skrub import TableReport

TableReport(electricity_lagged.skb.eval())

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Let’s extract the dates where the inter-quartile range of the load is greater than 15,000 MW.

electricity_lagged.filter(pl.col("load_mw_iqr_7d") > 15_000)[
    "time"
].dt.date().unique().sort().to_list().skb.eval()

[datetime.date(2021, 12, 26),
 datetime.date(2021, 12, 27),
 datetime.date(2021, 12, 28),
 datetime.date(2022, 1, 7),
 datetime.date(2022, 1, 8),
 datetime.date(2023, 1, 19),
 datetime.date(2023, 1, 20),
 datetime.date(2023, 1, 21),
 datetime.date(2024, 1, 10),
 datetime.date(2024, 1, 11),
 datetime.date(2024, 1, 12),
 datetime.date(2024, 1, 13)]

We observe 3 date ranges with high inter-quartile range. Let’s plot the electricity load and the lagged features for the first data range along with the weather data for Paris.

altair.Chart(
    electricity_lagged.filter(
        (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC"))
        & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC"))
    ).skb.eval()
).transform_fold(
    [
        "load_mw",
        "load_mw_iqr_7d",
    ],
).mark_line(
    tooltip=True
).encode(
    x="time:T", y="value:Q", color="key:N"
).interactive()

altair.Chart(
    all_city_weather.filter(
        (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC"))
        & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC"))
    ).skb.eval()
).transform_fold(
    [f"temperature_2m_{city_name}" for city_name in city_names],
).mark_line(
    tooltip=True
).encode(
    x="time:T", y="value:Q", color="key:N"
).interactive()

Based on the plots above, we can see that the electricity load was high just before the Christmas holidays due to low temperatures. Then the load suddenly dropped because temperatures went higher right at the start of the end-of-year holidays.

So those outliers do not seem to be caused to a data quality issue but rather due to a real change in the electricity load demand. We could conduct similar analysis for the other date ranges with high inter-quartile range but we will skip that for now.

If we had observed significant data quality issues over extended periods of time could have been addressed by removing the corresponding rows from the dataset. However, this would make the lagged and windowing feature engineering challenging to reimplement correctly. A better approach would be to keep a contiguous dataset assign 0 weights to the affected rows when fitting or evaluating the trained models via the use of the sample_weight parameter.

Final dataset#

We now assemble the dataset that will be used to train and evaluate the forecasting models via backtesting.

prediction_time = time = skrub.var(
    "prediction_time",
    pl.DataFrame().with_columns(
        pl.datetime_range(
            start=time_range_start + pl.duration(days=7),
            end=time_range_end - pl.duration(hours=24),
            time_zone="UTC",
            interval="1h",
        ).alias("prediction_time"),
    ),
)
prediction_time
<Var 'prediction_time'>
Show graph Var 'prediction_time'

Result:

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features = (
    (
        prediction_time.join(
            electricity_lagged, left_on="prediction_time", right_on="time"
        )
        .join(all_city_weather, left_on="prediction_time", right_on="time")
        .join(calendar, left_on="prediction_time", right_on="time")
    )
    .drop("prediction_time")
    .skb.mark_as_X()
)
features
<CallMethod 'drop'>
Show graph Var 'prediction_time' CallMethod 'join' Var 'electricity_raw' CallMethod 'with_columns' CallMethod 'drop' CallMethod 'rename' CallMethod 'filter' CallMethod 'filter' CallMethod 'filter' CallMethod 'select' CallMethod 'with_columns' CallMethod 'with_columns' CallMethod 'join' Var 'all_city_weather' CallMethod 'join' Var 'time' CallMethod 'with_columns' X: CallMethod 'drop'

Result:

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horizons = range(1, 25)  # Forecasting horizons from 1 to 24 hours
target_column_name_pattern = "load_mw_horizon_{horizon}h"

targets = prediction_time.join(
    electricity.with_columns(
        [
            pl.col("load_mw")
            .shift(-h)
            .alias(target_column_name_pattern.format(horizon=h))
            for h in horizons
        ]
    ),
    left_on="prediction_time",
    right_on="time",
)

horizon_of_interest = 12
target_column_name = target_column_name_pattern.format(horizon=horizon_of_interest)
predicted_target_column_name = "predicted_" + target_column_name
target = targets[target_column_name].skb.mark_as_y()

from sklearn.ensemble import HistGradientBoostingRegressor


predictions = features.skb.apply(
    HistGradientBoostingRegressor(
        random_state=0,
        learning_rate=skrub.choose_float(
            0.01, 0.9, default=0.1, log=True, name="learning_rate"
        ),
        max_leaf_nodes=skrub.choose_int(
            3, 300, default=30, log=True, name="max_leaf_nodes"
        ),
    ),
    y=target,
)
predictions
<Apply HistGradientBoostingRegressor>
Show graph Var 'prediction_time' CallMethod 'join' CallMethod 'join' Var 'electricity_raw' CallMethod 'with_columns' CallMethod 'drop' CallMethod 'rename' CallMethod 'filter' CallMethod 'filter' CallMethod 'filter' CallMethod 'select' CallMethod 'with_columns' CallMethod 'with_columns' CallMethod 'with_columns' CallMethod 'join' Var 'all_city_weather' CallMethod 'join' Var 'time' CallMethod 'with_columns' X: CallMethod 'drop' Apply HistGradientBoostingRegressor y: GetItem 'load_mw_horizon_12h'

Result:

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altair.Chart(
    pl.concat(
        [
            targets.skb.eval(),
            predictions.rename(
                {target_column_name: predicted_target_column_name}
            ).skb.eval(),
        ],
        how="horizontal",
    ).tail(24 * 7)
).transform_fold(
    [target_column_name, predicted_target_column_name],
).mark_line(
    tooltip=True
).encode(
    x="prediction_time:T", y="value:Q", color="key:N"
).interactive()

from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import make_scorer, mean_absolute_percentage_error, get_scorer

mape_scorer = make_scorer(mean_absolute_percentage_error)

ts_cv_5 = TimeSeriesSplit(n_splits=5, max_train_size=10_000, gap=24)

predictions.skb.cross_validate(
    cv=ts_cv_5,
    scoring={
        "r2": get_scorer("r2"),
        "mape": mape_scorer,
    },
    return_train_score=True,
    verbose=1,
    n_jobs=-1,
).round(3)

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.

[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    5.3s finished
fit_time score_time test_r2 train_r2 test_mape train_mape
0 1.110 0.066 0.834 0.994 0.050 0.012
1 1.395 0.066 0.942 0.993 0.039 0.014
2 1.469 0.063 0.931 0.991 0.041 0.014
3 1.415 0.050 0.954 0.991 0.032 0.016
4 1.032 0.038 0.937 0.990 0.037 0.016 
ts_cv_3 = TimeSeriesSplit(n_splits=3, max_train_size=10_000, gap=24)
randomized_search = predictions.skb.get_randomized_search(
    cv=ts_cv_3,
    scoring="r2",
    n_iter=30,
    fitted=True,
    verbose=1,
    n_jobs=-1,
)
randomized_search.results_

Fitting 3 folds for each of 30 candidates, totalling 90 fits
learning_rate max_leaf_nodes mean_test_score
0 0.113699 23 0.947179
1 0.208880 24 0.944733
2 0.156081 11 0.944494
3 0.057433 41 0.944074
4 0.081887 14 0.942966
5 0.161978 56 0.942341
6 0.213894 5 0.935727
7 0.062604 10 0.935273
8 0.256061 68 0.935229
9 0.341712 7 0.935039
10 0.160281 194 0.934038
11 0.240566 287 0.929032
12 0.310397 158 0.928710
13 0.051781 8 0.923933
14 0.469447 55 0.922337
15 0.511442 22 0.914329
16 0.041422 8 0.912852
17 0.024679 28 0.911136
18 0.047793 6 0.907094
19 0.036359 9 0.906045
20 0.573683 260 0.905198
21 0.591834 215 0.901900
22 0.623573 63 0.898332
23 0.017368 65 0.882845
24 0.014462 102 0.856322
25 0.018739 10 0.837700
26 0.012818 104 0.832090
27 0.012360 28 0.798690
28 0.010728 46 0.774766
29 0.010907 3 0.604785 
randomized_search.plot_results()

# nested_cv_results = skrub.cross_validate(
#     environment=predictions.skb.get_data(),
#     pipeline=randomized_search,
#     cv=ts_cv_5,
#     scoring={
#         "r2": get_scorer("r2"),
#         "mape": mape_scorer,
#     },
#     n_jobs=-1,
#     return_pipeline=True,
# ).round(3)
# nested_cv_results

# for outer_cv_idx in range(len(nested_cv_results)):
#     print(
#         nested_cv_results.loc[outer_cv_idx, "pipeline"]
#         .results_.loc[0]
#         .round(3)
#         .to_dict()
#     )

# from joblib import Parallel, delayed

# cv_predictions = []
# for ts_cv_train_idx, ts_cv_test_idx in ts_cv_5.split(prediction_time.skb.eval()):
#     features[ts_cv_train_idx].fit

predictions = features.skb.apply(
    skrub.SelectCols(
        cols=skrub.choose_from(
            [
                skrub.selectors.all(),
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_"),
                ),
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_")
                    or name.startswith("temperature_"),
                ),
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_")
                    or name.startswith("precipitation_"),
                ),
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_")
                    or name.startswith("wind_speed_"),
                ),
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_")
                    or name.startswith("cloud_cover_"),
                ),
                # calendar features
                skrub.selectors.filter_names(
                    lambda name: name.startswith("load_mw_") or name.endswith("_fr"),
                ),
            ],
            name="feature_subset",
        )
    )
).skb.apply(
    HistGradientBoostingRegressor(
        random_state=0,
        learning_rate=skrub.choose_float(
            0.01, 0.9, default=0.1, log=True, name="learning_rate"
        ),
    ),
    y=target,
)

randomized_search = predictions.skb.get_randomized_search(
    cv=ts_cv_3,
    scoring="r2",
    n_iter=30,
    fitted=True,
    verbose=1,
    n_jobs=-1,
)
randomized_search.results_

Fitting 3 folds for each of 30 candidates, totalling 90 fits
feature_subset learning_rate mean_test_score
0 all() 0.350962 0.934246
1 filter_names(<lambda>) 0.176523 0.930727
2 filter_names(<lambda>) 0.319901 0.928186
3 filter_names(<lambda>) 0.491934 0.918374
4 all() 0.024848 0.912311
5 all() 0.020155 0.891431
6 filter_names(<lambda>) 0.019220 0.887680
7 filter_names(<lambda>) 0.014875 0.847049
8 filter_names(<lambda>) 0.143718 0.845938
9 filter_names(<lambda>) 0.287886 0.837412
10 filter_names(<lambda>) 0.025463 0.828014
11 filter_names(<lambda>) 0.147548 0.821068
12 filter_names(<lambda>) 0.190049 0.820787
13 filter_names(<lambda>) 0.022037 0.820126
14 filter_names(<lambda>) 0.059380 0.816499
15 filter_names(<lambda>) 0.082289 0.814294
16 filter_names(<lambda>) 0.524694 0.813750
17 filter_names(<lambda>) 0.057686 0.813077
18 filter_names(<lambda>) 0.372401 0.803940
19 filter_names(<lambda>) 0.027287 0.797866
20 filter_names(<lambda>) 0.439953 0.797371
21 filter_names(<lambda>) 0.027580 0.794893
22 filter_names(<lambda>) 0.026319 0.794393
23 filter_names(<lambda>) 0.356663 0.790736
24 filter_names(<lambda>) 0.502451 0.787887
25 filter_names(<lambda>) 0.498268 0.787832
26 filter_names(<lambda>) 0.018848 0.768831
27 filter_names(<lambda>) 0.633333 0.763354
28 filter_names(<lambda>) 0.728635 0.722056
29 filter_names(<lambda>) 0.010028 0.667794 
# nested_cv_results = skrub.cross_validate(
#     environment=predictions.skb.get_data(),
#     pipeline=randomized_search,
#     cv=ts_cv_5,
#     scoring={
#         "r2": get_scorer("r2"),
#         "mape": mape_scorer,
#     },
#     n_jobs=-1,
#     return_pipeline=True,
# ).round(3)
# nested_cv_results
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